Gastos Casa
27 January, 2025
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 26/11/2024 | Enceres | 12990 | Tami | Confort 48 rollos |
| 27/11/2024 | Transporte | 21780 | Tami | Transvip aeropuerto |
| 27/11/2024 | Electricidad | 45000 | Andrés | Eléctrico |
| 30/11/2024 | Comida | 62899 | Tami | Supermercado |
| 11/12/2024 | Comida | 17738 | Andrés | compra lider despues viaje |
| 12/12/2024 | Comida | 55616 | Tami | Supermercado |
| 14/12/2024 | Diosi | 16276 | Andrés | antiparasitario |
| 15/12/2024 | Comida | 23773 | Tami | Supermercado |
| 18/12/2024 | Comida | 41554 | Tami | Supermercado |
| 18/12/2024 | VTR | 22000 | Andrés | NA |
| 20/12/2024 | Plata basureros | 10000 | Tami | NA |
| 3/12/2024 | Agua | 15828 | Andrés | PAC AGUAS ANDIN 000000005687837 |
| 22/12/2024 | Comida | 46608 | Tami | Supermercado |
| 10/12/2024 | Otros | 47484 | Andrés | viaje brasil |
| 29/12/2024 | Electricidad | 27417 | Andrés | NA |
| 29/12/2024 | Comida | 83284 | Tami | Supermercado |
| 30/12/2024 | Comida | 30000 | Andrés | nueces almendras mix etc |
| 30/12/2024 | Otros | 47484 | Andrés | viaje a brasil (duplicar para q cargue sobre tami) |
| 4/1/2025 | Diosi | 53999 | Andrés | n y d pumpkin 7.5 |
| 4/1/2025 | Comida | 15260 | Andrés | NA |
| 6/1/2025 | Comida | 40988 | Tami | Supermercado |
| 8/1/2025 | Pago cámaras MB | 20000 | Tami | NA |
| 13/1/2025 | Comida | 67387 | Tami | Supermercado |
| 20/1/2025 | Comida | 21692 | Andrés | gnoccis |
| 20/1/2025 | Comida | 86884 | Tami | Supermercado |
| 21/1/2025 | Comida | 21525 | Andrés | piwen |
| 23/1/2025 | VTR | 21990 | Andrés | NA |
| 25/1/2025 | Diosi | 20000 | Andrés | arena diosi |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 9.8774e+08 2 4.9507 0.0073 **
## lag_depvar 2.6098e+11 1 2616.1076 <2e-16 ***
## Residuals 8.0205e+10 804
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1957.102 16414.78 0.1548772
## 2-0 31610.073 23334.038 39886.11 0.0000000
## 2-1 24381.235 19579.509 29182.96 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 633 77359.43 2 88593.71
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## 635 81753.00 2 79878.71
## 636 75716.00 2 81753.00
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## 639 49682.86 2 63528.57
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## 651 104812.71 2 89422.14
## 652 98812.71 2 104812.71
## 653 64779.86 2 98812.71
## 654 61862.86 2 64779.86
## 655 58376.43 2 61862.86
## 656 59503.57 2 58376.43
## 657 55429.43 2 59503.57
## 658 44454.57 2 55429.43
## 659 47184.00 2 44454.57
## 660 52126.71 2 47184.00
## 661 51202.00 2 52126.71
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## 663 64297.14 2 64437.14
## 664 64628.57 2 64297.14
## 665 51413.14 2 64628.57
## 666 52969.43 2 51413.14
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## 669 41907.86 2 48799.43
## 670 45382.00 2 41907.86
## 671 42633.29 2 45382.00
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## 673 44051.86 2 46624.71
## 674 35852.86 2 44051.86
## 675 29737.71 2 35852.86
## 676 29734.86 2 29737.71
## 677 32881.71 2 29734.86
## 678 38298.57 2 32881.71
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## 681 38628.86 2 38601.86
## 682 39142.57 2 38628.86
## 683 32666.14 2 39142.57
## 684 39911.57 2 32666.14
## 685 39336.29 2 39911.57
## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
## 688 54220.57 2 41963.14
## 689 63901.86 2 54220.57
## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
## 692 56293.86 2 60863.86
## 693 52725.00 2 56293.86
## 694 58625.00 2 52725.00
## 695 47513.00 2 58625.00
## 696 40300.14 2 47513.00
## 697 33312.43 2 40300.14
## 698 29556.71 2 33312.43
## 699 27816.71 2 29556.71
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## 703 39694.14 2 32902.57
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## 705 79551.14 2 72501.29
## 706 99637.71 2 79551.14
## 707 95424.29 2 99637.71
## 708 98395.14 2 95424.29
## 709 115594.71 2 98395.14
## 710 114267.57 2 115594.71
## 711 88353.29 2 114267.57
## 712 88750.86 2 88353.29
## 713 78835.71 2 88750.86
## 714 75519.14 2 78835.71
## 715 73202.86 2 75519.14
## 716 53433.29 2 73202.86
## 717 48165.71 2 53433.29
## 718 52163.14 2 48165.71
## 719 49306.86 2 52163.14
## 720 36846.86 2 49306.86
## 721 43220.57 2 36846.86
## 722 38952.29 2 43220.57
## 723 41522.29 2 38952.29
## 724 39090.00 2 41522.29
## 725 28452.57 2 39090.00
## 726 32975.00 2 28452.57
## 727 33690.71 2 32975.00
## 728 26405.29 2 33690.71
## 729 47087.43 2 26405.29
## 730 49660.29 2 47087.43
## 731 47409.71 2 49660.29
## 732 53881.71 2 47409.71
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## 734 45503.86 2 45189.57
## 735 54640.14 2 45503.86
## 736 39131.29 2 54640.14
## 737 35024.14 2 39131.29
## 738 44755.43 2 35024.14
## 739 41063.29 2 44755.43
## 740 42783.29 2 41063.29
## 741 45952.57 2 42783.29
## 742 44937.43 2 45952.57
## 743 40838.43 2 44937.43
## 744 48838.43 2 40838.43
## 745 43139.14 2 48838.43
## 746 67134.29 2 43139.14
## 747 73224.29 2 67134.29
## 748 68770.71 2 73224.29
## 749 59539.29 2 68770.71
## 750 82179.86 2 59539.29
## 751 74252.14 2 82179.86
## 752 73015.00 2 74252.14
## 753 56116.43 2 73015.00
## 754 111885.00 2 56116.43
## 755 131425.14 2 111885.00
## 756 136678.00 2 131425.14
## 757 115531.29 2 136678.00
## 758 118310.86 2 115531.29
## 759 117449.43 2 118310.86
## 760 115193.57 2 117449.43
## 761 61025.43 2 115193.57
## 762 43913.86 2 61025.43
## 763 46099.29 2 43913.86
## 764 44524.86 2 46099.29
## 765 42208.71 2 44524.86
## 766 166486.57 2 42208.71
## 767 171565.29 2 166486.57
## 768 200415.71 2 171565.29
## 769 204498.14 2 200415.71
## 770 197558.86 2 204498.14
## 771 195266.57 2 197558.86
## 772 203144.29 2 195266.57
## 773 85493.71 2 203144.29
## 774 74721.57 2 85493.71
## 775 36232.14 2 74721.57
## 776 40161.71 2 36232.14
## 777 40629.86 2 40161.71
## 778 45663.71 2 40629.86
## 779 39252.29 2 45663.71
## 780 39618.57 2 39252.29
## 781 39438.43 2 39618.57
## 782 44650.71 2 39438.43
## 783 38626.71 2 44650.71
## 784 38280.43 2 38626.71
## 785 44134.14 2 38280.43
## 786 47596.43 2 44134.14
## 787 45598.43 2 47596.43
## 788 42564.29 2 45598.43
## 789 45699.14 2 42564.29
## 790 49553.86 2 45699.14
## 791 50018.43 2 49553.86
## 792 43772.86 2 50018.43
## 793 39235.43 2 43772.86
## 794 39905.00 2 39235.43
## 795 40374.43 2 39905.00
## 796 34230.57 2 40374.43
## 797 34324.14 2 34230.57
## 798 33491.57 2 34324.14
## 799 33366.43 2 33491.57
## 800 46646.86 2 33366.43
## 801 49770.86 2 46646.86
## 802 57339.86 2 49770.86
## 803 59799.14 2 57339.86
## 804 53577.14 2 59799.14
## 805 61775.29 2 53577.14
## 806 70627.86 2 61775.29
## 807 57888.43 2 70627.86
## 808 49960.71 2 57888.43
## 809 42923.71 2 49960.71
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 652 53844.34 22505.959
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2023.958924 4042.545685 -540.194651 2436.231306 -2973.829431
## 7 8 9 10 11
## 517.220304 -5658.091639 -1185.164791 -3963.216095 -412.288706
## 12 13 14 15 16
## -4934.834596 -1601.121806 -891.501231 385.066120 -3237.006332
## 17 18 19 20 21
## -370.268809 -2123.515032 6611.391929 -1529.445132 -1207.614180
## 22 23 24 25 26
## 1476.820695 -1187.379546 234.568504 1694.257676 -7104.686273
## 27 28 29 30 31
## 951.344735 8194.512191 412.319739 -19.843696 -2406.018852
## 32 33 34 35 36
## 1573.287363 4568.191262 1118.682325 2382.916655 -1877.927677
## 37 38 39 40 41
## 4600.633063 4299.455190 -2283.077586 -2985.814738 -1111.276983
## 42 43 44 45 46
## -10741.454478 7299.072349 2559.064112 1366.060909 8103.204002
## 47 48 49 50 51
## 677.052793 6520.170105 6700.947649 -5900.190196 -4805.696512
## 52 53 54 55 56
## -5064.336751 -7928.000592 6137.503174 -4075.506214 -4889.689620
## 57 58 59 60 61
## 3864.266955 891.656432 -29.637897 144.368649 -4994.737520
## 62 63 64 65 66
## 18132.767754 3629.355633 -3659.303126 5917.180931 7331.553244
## 67 68 69 70 71
## 14621.379471 1664.715918 -13239.019816 -1316.911801 4635.397694
## 72 73 74 75 76
## -4911.542154 -4409.788547 -10497.875334 2476.416169 -5393.493342
## 77 78 79 80 81
## 1074.437029 -6857.801028 560.876746 -2342.772712 -2681.225774
## 82 83 84 85 86
## -3918.177454 -521.720135 2329.026034 3773.116119 482.418768
## 87 88 89 90 91
## -480.249932 200.756621 4305.311390 -1164.194932 1150.873050
## 92 93 94 95 96
## -2065.578873 -1043.337019 179.381479 276.156700 -7483.129417
## 97 98 99 100 101
## 2399.738034 -8597.756139 -2928.014428 -4025.421872 -1720.293386
## 102 103 104 105 106
## -1245.036119 3196.641716 -2331.326935 2605.718172 -1150.663627
## 107 108 109 110 111
## 978.370519 2593.078409 -3151.666351 -4717.608887 -840.881870
## 112 113 114 115 116
## 1912.601151 11699.670879 -1248.566139 2664.512999 4256.781108
## 117 118 119 120 121
## 3493.318259 -1111.436113 -4725.239400 -3727.241900 2320.708318
## 122 123 124 125 126
## -1733.994877 1340.999987 8857.524467 837.983922 121.729343
## 127 128 129 130 131
## -2528.945602 2650.855979 7046.341454 1000.294276 -8510.941949
## 132 133 134 135 136
## 1747.355375 4132.240994 -3170.778741 -1422.613912 -855.090873
## 137 138 139 140 141
## -3880.116801 1186.747172 -493.345784 -2911.227692 1723.093464
## 142 143 144 145 146
## -1878.410778 -7825.128963 2050.709261 -3471.852278 2112.452163
## 147 148 149 150 151
## -250.623398 1029.214794 -354.939110 1356.305119 1188.864385
## 152 153 154 155 156
## 3357.330868 -4864.426673 -1172.080171 -3232.595095 5962.655946
## 157 158 159 160 161
## 9746.024745 -3657.693075 -5002.060008 3383.480355 -31.273105
## 162 163 164 165 166
## 2468.507399 -6142.381541 -6974.071867 3935.624705 17164.495416
## 167 168 169 170 171
## 3372.185924 -658.411900 -2704.042532 -1357.804449 3338.864593
## 172 173 174 175 176
## -484.568761 -8330.753535 2619.865782 4076.767747 370.428865
## 177 178 179 180 181
## 8494.759794 -9516.154397 -3724.683531 -10992.732931 -11473.913776
## 182 183 184 185 186
## 1014.365060 9067.000364 -1673.061473 5685.956430 6301.447771
## 187 188 189 190 191
## 12892.152420 8141.786832 -4366.183411 2169.214837 10069.087048
## 192 193 194 195 196
## -1960.217509 -2755.870422 -10585.402951 -6648.333890 960.152423
## 197 198 199 200 201
## -5508.955802 -10062.183745 5134.331773 -3328.374407 -1967.768336
## 202 203 204 205 206
## -1057.666952 6240.619341 9612.992248 287.575918 2632.907251
## 207 208 209 210 211
## 2801.163632 5482.955467 12523.147187 -6019.367584 -11611.606781
## 212 213 214 215 216
## -5955.886058 -10864.015682 -5330.575824 1280.161207 -13261.914227
## 217 218 219 220 221
## 16161.590792 7545.082529 1246.683012 26403.687643 12186.753490
## 222 223 224 225 226
## 6974.613995 13658.639899 -4301.831447 -2110.474943 3420.753049
## 227 228 229 230 231
## 4.536152 2399.299534 8661.525831 5479.733714 -2256.265269
## 232 233 234 235 236
## -2163.958257 9101.517150 -11844.445041 -7589.950362 -8829.826715
## 237 238 239 240 241
## -10371.868465 2826.625769 1093.261975 -8559.565730 -9236.427961
## 242 243 244 245 246
## 8863.234179 -8019.030676 2247.244689 -10549.561367 -4284.270884
## 247 248 249 250 251
## 1195.925084 768.206125 -12557.266812 3423.254243 1828.634575
## 252 253 254 255 256
## 3968.578981 1878.407037 -1424.486474 10875.344600 20589.727450
## 257 258 259 260 261
## 2856.856659 -4615.370467 3779.409114 -2030.594722 3409.216704
## 262 263 264 265 266
## -5184.617930 -11211.328137 -5018.840270 -801.043912 -5467.453222
## 267 268 269 270 271
## 8509.435401 -4573.038987 3905.839738 -2402.571631 4139.433918
## 272 273 274 275 276
## 405.358855 6997.222282 -1736.590835 11705.270230 -4935.368923
## 277 278 279 280 281
## 1388.589739 -711.587264 7515.604321 -5411.995159 -3067.319651
## 282 283 284 285 286
## -11586.186018 -2959.464540 18372.132417 7446.709928 2377.751017
## 287 288 289 290 291
## -988.409347 552.823414 6046.708822 6516.402044 -19152.910696
## 292 293 294 295 296
## -11453.828204 -8398.015979 9414.304607 2789.764260 -1470.795384
## 297 298 299 300 301
## 27114.614860 9690.949218 4501.670769 9113.096048 2432.164092
## 302 303 304 305 306
## -1452.933834 7492.207895 -24713.381964 -3857.721741 -480.274733
## 307 308 309 310 311
## -7268.180230 -4244.239061 2675.010480 -9459.007822 -3463.820229
## 312 313 314 315 316
## -8409.727145 1368.505814 -3358.393489 1847.391589 -4296.638252
## 317 318 319 320 321
## 27240.136173 -1053.004866 2967.374805 10497.265330 5220.972219
## 322 323 324 325 326
## 32000.063251 4626.110096 -21417.315817 1409.696024 733.076851
## 327 328 329 330 331
## -6835.122258 -2070.020889 -33589.419249 724.977002 -2464.537043
## 332 333 334 335 336
## -248.957367 -3326.669742 3935.121493 -608.578774 -7125.957109
## 337 338 339 340 341
## -3266.240483 -2334.571723 -7819.984898 3734.821897 -1513.049608
## 342 343 344 345 346
## -1881.518299 -1137.754020 29.654479 326.962628 -1781.844754
## 347 348 349 350 351
## -9609.731380 -13342.012210 2221.547251 -4434.406741 -3763.116478
## 352 353 354 355 356
## -6081.170531 1661.846563 1274.164212 2624.364123 -3918.606886
## 357 358 359 360 361
## -662.368049 523.587819 6847.863522 72.590740 -247.907569
## 362 363 364 365 366
## 2369.859661 -2977.600817 -1093.368647 -8956.568044 -4800.498933
## 367 368 369 370 371
## -6369.788448 -5084.274269 -7372.714583 4917.857593 237.770921
## 372 373 374 375 376
## 6974.621326 -7822.933447 -2418.998246 -3539.821955 -2609.938107
## 377 378 379 380 381
## -12596.346483 1812.470245 -10746.215446 5620.978230 9218.993239
## 382 383 384 385 386
## 2953.027466 -2593.982907 1415.063792 6541.158109 11170.862550
## 387 388 389 390 391
## -6099.325746 -5633.679453 -404.703176 8314.832741 1524.038007
## 392 393 394 395 396
## 10923.496841 -10228.780886 2474.573237 401.913174 251.565543
## 397 398 399 400 401
## -963.712820 -866.199350 -14784.310199 8304.284870 -1437.746787
## 402 403 404 405 406
## -1620.187786 6743.040810 -8204.421464 -1524.950777 -2750.703054
## 407 408 409 410 411
## -6023.588795 -3033.295115 -4078.780671 -8900.771123 6025.999789
## 412 413 414 415 416
## 1492.066707 -7537.415013 -7825.045884 14118.378871 3627.086844
## 417 418 419 420 421
## 4274.989112 -8280.997153 -4948.474302 -2782.278868 2649.337808
## 422 423 424 425 426
## -14199.792028 -2912.457483 -9213.707149 2934.803406 6870.306682
## 427 428 429 430 431
## 6420.868019 -4183.929653 -4300.493141 -4885.012732 -1933.819999
## 432 433 434 435 436
## -5853.992211 -6747.506217 -6046.823623 -1472.639089 -933.832442
## 437 438 439 440 441
## -5070.132616 2498.807798 4729.710370 -5202.517561 -2286.816633
## 442 443 444 445 446
## 1449.320464 -3981.390222 2702.697639 -6733.790660 -12240.056406
## 447 448 449 450 451
## -4590.392958 9575.087541 -2162.627208 4625.717753 -6029.262852
## 452 453 454 455 456
## -1258.872841 246.111817 2879.858699 -12435.522254 3256.122873
## 457 458 459 460 461
## -6839.532549 6408.765855 2858.592348 2333.516512 -4034.795477
## 462 463 464 465 466
## 1920.060170 -193.360986 1604.816926 -719.867257 3153.250841
## 467 468 469 470 471
## -2853.297721 5604.822264 -7169.601577 -3154.871144 -2380.944810
## 472 473 474 475 476
## -4829.494580 2850.994163 7633.480313 -6220.560230 1310.333742
## 477 478 479 480 481
## -6361.058369 -2998.140815 1868.326457 -13087.251198 -9856.994723
## 482 483 484 485 486
## -1269.310102 -54.176464 -1049.004181 -1435.398712 -9682.645130
## 487 488 489 490 491
## 11030.256723 6106.617896 7256.152209 -5638.215252 5190.753418
## 492 493 494 495 496
## 9089.772308 5808.437200 -13742.151421 -10765.188320 -3590.537049
## 497 498 499 500 501
## -1242.680581 -660.631440 -7764.495155 502.968081 4169.740764
## 502 503 504 505 506
## 5364.918358 487.588605 -96.528646 -7417.249619 423.846860
## 507 508 509 510 511
## -5200.479908 1700.091543 -1442.024315 -8301.379943 -713.297685
## 512 513 514 515 516
## -2790.286440 -698.538714 1216.537275 -9623.841031 -7857.863667
## 517 518 519 520 521
## 24218.236248 9636.669041 5646.168717 -5591.568655 2572.246638
## 522 523 524 525 526
## 16783.562596 11165.361555 -24498.021047 -5285.186971 -3932.194517
## 527 528 529 530 531
## 4391.187605 -559.180584 -11302.524478 4242.469724 13737.101716
## 532 533 534 535 536
## -5213.877939 4162.629988 5325.289868 -2042.306031 -4779.419134
## 537 538 539 540 541
## -7287.384482 -2279.065193 8153.242637 -80.887825 -8348.096208
## 542 543 544 545 546
## 1648.021923 -777.197854 190.667353 -11209.007630 -11190.998367
## 547 548 549 550 551
## 1955.286753 6899.015943 -1461.531692 694.647146 -7871.083563
## 552 553 554 555 556
## 8445.450917 742.263614 -12115.254500 9042.347838 8488.995582
## 557 558 559 560 561
## -111.940121 4642.589580 -3805.787307 13896.119280 21223.671166
## 562 563 564 565 566
## -6832.143840 -10001.263201 6510.359283 -62.248357 3174.437795
## 567 568 569 570 571
## -7664.304238 -17551.249584 6505.582641 6247.047679 1687.597999
## 572 573 574 575 576
## 2879.158521 1541.417712 -2396.108843 14501.083165 -9927.826626
## 577 578 579 580 581
## -6471.955799 8515.896249 2625.356228 -6786.463205 7302.938006
## 582 583 584 585 586
## -4036.702508 -2989.899652 15504.270863 -14765.773624 8233.579406
## 587 588 589 590 591
## -159.992458 -6437.677168 -946.503888 65.313691 -10839.256696
## 592 593 594 595 596
## 1659.645161 -7292.549138 2948.968682 8729.065502 -7680.651677
## 597 598 599 600 601
## 5704.615387 2559.403585 6667.992126 -3407.019540 5950.793263
## 602 603 604 605 606
## -8521.550101 2073.754226 1079.837079 2944.701608 1290.450440
## 607 608 609 610 611
## 189.886861 -6015.662276 7898.916236 -1393.319120 -2773.703413
## 612 613 614 615 616
## -3637.554786 -8394.758837 11828.572259 4752.361962 -9517.206740
## 617 618 619 620 621
## 11467.752778 5841.947947 -5801.399793 26162.891055 -13126.362745
## 622 623 624 625 626
## -7009.393522 2964.824811 -4360.185066 -10770.601405 11164.772021
## 627 628 629 630 631
## -21815.684785 -2504.914406 8588.334615 11006.721611 -1728.954913
## 632 633 634 635 636
## 33116.585303 -6886.658099 5460.051175 5130.539655 -2546.030501
## 637 638 639 640 641
## -5599.611439 -2161.630009 -12636.977898 -2393.010379 -2027.920891
## 642 643 644 645 646
## -2655.390459 -2984.681773 1697.179864 4302.346904 16816.273401
## 647 648 649 650 651
## 18336.703717 599.490534 4514.681755 10330.954735 19841.942605
## 652 653 654 655 656
## 378.722236 -28405.510930 -1551.565932 -2486.288480 1690.679809
## 657 658 659 660 661
## -3369.454552 -10780.371048 1549.540580 4104.630825 -1143.824912
## 662 663 664 665 666
## 12900.230874 1182.516185 1636.412649 -11868.939913 1247.815011
## 667 668 669 670 671
## 1052.279068 -5303.435700 -7527.355839 1975.331524 -3812.461012
## 672 673 674 675 676
## 2583.462177 -3480.979582 -9429.319634 -8372.217828 -3025.727400
## 677 678 679 680 681
## 123.629087 2787.707922 636.771637 -3911.045651 -1885.819522
## 682 683 684 685 686
## -1395.724045 -8321.534802 4589.283483 -2324.090585 -1478.276091
## 687 688 689 690 691
## 506.338188 10765.540631 9724.387430 10469.625567 -9842.779144
## 692 693 694 695 696
## -3694.964017 -3266.119231 5755.812297 -10517.334613 -8009.740151
## 697 698 699 700 701
## -8687.858614 -6330.925443 -4785.536768 3040.135614 -4461.758028
## 702 703 704 705 706
## -1952.963885 4165.034133 31031.109551 9382.240593 23301.803817
## 707 708 709 710 711
## 1517.231844 8173.872691 22774.625448 6401.802093 -18351.537985
## 712 713 714 715 716
## 4715.090333 -5547.836346 -190.931686 394.021986 -17349.330653
## 717 718 719 720 721
## -5323.060981 3282.284541 -3070.834415 -13032.239467 4241.117276
## 722 723 724 725 726
## -5602.706708 701.064415 -3979.381902 -12489.118287 1338.620726
## 727 728 729 730 731
## -1901.752827 -9813.267259 17241.954778 1722.680107 -2778.551270
## 732 733 734 735 736
## 5662.182590 -8691.475996 -773.558714 8087.799078 -15413.212775
## 737 738 739 740 741
## -5953.662390 7370.431403 -4834.354708 115.423546 1780.103721
## 742 743 744 745 746
## -2007.437301 -5218.420111 6367.264835 -6330.186181 22650.524618
## 747 748 749 750 751
## 7750.277598 -2030.647167 -7366.222108 23349.732210 -4383.289750
## 752 753 754 755 756
## 1314.499273 -14501.855879 56049.090078 26804.522746 14964.236171
## 757 758 759 760 761
## -10777.523439 10500.573261 7207.657148 5705.352448 -46489.432761
## 762 763 764 765 766
## -16216.301962 937.827288 -2548.350083 -3487.229015 122816.721950
## 767 768 769 770 771
## 19180.812777 43588.531087 22433.451136 11922.976674 15700.974523
## 772 773 774 775 776
## 25583.913103 -98957.851673 -6812.726187 -35879.000212 1719.994157
## 777 778 779 780 781
## -1249.336789 3375.002716 -7439.896404 -1465.081069 -1965.639923
## 782 783 784 785 786
## 3404.229477 -7179.325151 -2255.992393 3900.642476 2242.270665
## 787 788 789 790 791
## -2784.435304 -4070.786377 1718.249924 2830.683150 -76.736392
## 792 793 794 795 796
## -6728.701277 -5802.687191 -1163.906364 -1280.199235 -7834.698720
## 797 798 799 800 801
## -2366.661307 -3281.086276 -2677.920073 10711.979799 2218.650496
## 802 803 804 805 806
## 7054.866945 2893.013513 -5480.297481 8160.668440 9841.745008
## 807 808 809
## -10641.653339 -7425.289250 -7527.357371
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17245.33 20096.45 24356.34 24073.91 26430.54 23759.49 24476.81 19702.31
## 10 11 12 13 14 15 16 17
## 19438.50 16777.57 17556.12 14280.98 14332.22 14997.79 16696.72 15014.41
## 18 19 20 21 22 23 24 25
## 16050.52 15423.18 22515.45 21598.19 21077.32 22969.95 22295.00 22948.46
## 26 27 28 29 30 31 32 33
## 24796.97 18716.94 20445.49 28293.68 28351.42 28023.88 25650.00 27054.38
## 34 35 36 37 38 39 40 41
## 30902.75 31251.65 32662.78 30169.94 34143.54 37356.08 34408.10 31214.56
## 42 43 44 45 46 47 48 49
## 30060.74 20627.21 28156.36 30596.22 31686.94 38534.52 38028.40 42697.05
## 50 51 52 53 54 55 56 57
## 46939.19 39626.98 34187.91 29203.71 22338.64 28637.36 25213.26 21505.73
## 58 59 60 61 62 63 64 65
## 25920.20 27181.50 27478.92 27891.31 23756.52 40370.79 42217.30 37456.68
## 66 67 68 69 70 71 72 73
## 41669.45 46591.91 57274.86 55285.88 40508.63 38011.03 41033.11 35325.36
## 74 75 76 77 78 79 80 81
## 30771.30 21461.87 24667.78 20587.85 22676.80 17565.27 19583.49 18808.94
## 82 83 84 85 86 87 88 89
## 17835.32 15901.58 17181.12 20794.17 25218.01 26209.25 26234.24 26851.83
## 90 91 92 93 94 95 96 97
## 30982.62 29811.56 30812.29 28874.05 28072.76 28441.41 28848.56 22417.12
## 98 99 100 101 102 103 104 105
## 25436.33 18457.16 17311.71 15349.72 15649.89 16328.22 20807.04 19889.28
## 106 107 108 109 110 111 112 113
## 23405.24 23194.92 24873.35 27754.09 25248.75 21687.31 21963.11 24613.04
## 114 115 116 117 118 119 120 121
## 35492.57 33682.92 35522.93 38525.40 40484.01 38169.24 32983.10 29319.43
## 122 123 124 125 126 127 128 129
## 31405.14 29682.71 30865.90 38476.16 38118.13 37178.37 34037.57 35821.23
## 130 131 132 133 134 135 136 137
## 41226.56 40666.08 31855.64 33122.19 36316.35 32722.04 31107.09 30190.83
## 138 139 140 141 142 143 144 145
## 26743.11 28159.49 27928.80 25611.91 27639.13 26261.99 19855.29 22890.00
## 146 147 148 149 150 151 152 153
## 20713.69 23694.91 24235.64 25828.22 26010.55 27666.99 28969.53 32005.86
## 154 155 156 157 158 159 160 161
## 27469.79 26731.74 24283.63 30185.83 41678.12 40006.06 37367.38 42394.56
## 162 163 164 165 166 167 168 169
## 43805.06 47225.67 42685.36 37986.09 43418.79 59743.39 61958.55 60370.47
## 170 171 172 173 174 175 176 177
## 57191.80 55588.85 58295.14 57317.90 49599.42 52426.80 56174.57 56210.81
## 178 179 180 181 182 183 184 185
## 63349.44 53838.68 50585.16 41381.20 32908.92 36422.00 46539.35 45994.61
## 186 187 188 189 190 191 192 193
## 51955.55 57708.42 68506.21 73796.33 67482.36 67676.06 74756.07 70426.58
## 194 195 196 197 198 199 200 201
## 65943.26 55172.33 49194.28 50620.53 46209.18 38367.24 44800.80 43025.77
## 202 203 204 205 206 207 208 209
## 42663.24 43142.24 49945.58 58847.00 58476.09 60203.26 61861.33 65657.71
## 210 211 212 213 214 215 216 217
## 75137.22 67209.18 55382.03 49983.44 40967.43 37920.98 41038.91 31045.41
## 218 219 220 221 222 223 224 225
## 48042.20 55373.03 56276.17 79072.82 86578.10 88584.07 96185.83 87124.33
## 226 227 228 229 230 231 232 233
## 81114.53 80695.89 77341.27 76501.62 81245.12 82611.27 77039.10 72245.48
## 234 235 236 237 238 239 240 241
## 77906.87 64536.38 56561.97 48501.58 40101.66 44299.31 46454.99 39896.71
## 242 243 244 245 246 247 248 249
## 33567.62 43864.17 38103.18 42044.28 34297.56 33001.65 36661.94 39489.70
## 250 251 252 253 254 255 256 257
## 30306.60 36252.79 40059.42 45261.31 47983.34 47475.23 57790.27 75311.43
## 258 259 260 261 262 263 264 265
## 75126.23 68427.73 69911.59 66127.21 67575.33 61324.47 50584.41 46606.33
## 266 267 268 269 270 271 272 273
## 46816.02 42917.42 51733.61 48001.59 52154.00 50267.99 54340.93 54637.35
## 274 275 276 277 278 279 280 281
## 60663.02 58294.02 67980.23 61896.70 62107.02 60453.82 66204.57 59926.46
## 282 283 284 285 286 287 288 289
## 56485.61 46023.61 44418.15 61674.00 67211.68 67621.70 65035.75 64121.86
## 290 291 292 293 294 295 296 297
## 68128.31 72043.91 53014.40 43102.87 37105.70 47441.24 50687.51 49800.24
## 298 299 300 301 302 303 304 305
## 74029.77 79983.33 80651.90 85270.69 83466.79 78490.22 81961.81 56826.15
## 306 307 308 309 310 311 312 313
## 53082.13 52761.47 46543.10 43748.70 47357.01 39898.96 38619.30 33173.35
## 314 315 316 317 318 319 320 321
## 36963.11 36143.32 39980.07 37961.72 63783.58 61621.77 63247.59 71256.74
## 322 323 324 325 326 327 328 329
## 73647.37 99164.18 97539.60 73336.45 72132.64 70487.69 62428.31 59546.56
## 330 331 332 333 334 335 336 337
## 29453.45 33146.11 33586.24 35909.38 35249.31 41024.29 42101.39 37342.38
## 338 339 340 341 342 343 344 345
## 36555.71 36682.56 31995.04 38002.34 38666.66 38925.47 39802.49 41590.89
## 346 347 348 349 350 351 352 353
## 43415.42 43166.73 36101.58 26656.31 32008.41 30867.83 30457.31 28070.44
## 354 355 356 357 358 359 360 361
## 32755.84 36515.35 40985.18 39171.65 40433.70 42575.14 49980.69 50532.05
## 362 363 364 365 366 367 368 369
## 50734.00 53200.60 50680.51 50124.28 42759.21 39952.07 36123.70 33899.29
## 370 371 372 373 374 375 376 377
## 29951.57 37249.66 39539.81 47436.36 41399.57 40845.96 39381.22 38913.35
## 378 379 380 381 382 383 384 385
## 29768.24 34372.79 27414.74 35645.58 45993.12 49563.55 47834.51 49828.98
## 386 387 388 389 390 391 392 393
## 56057.85 65556.61 58758.39 53218.85 52947.17 60337.10 60861.22 69542.07
## 394 395 396 397 398 399 400 401
## 58632.43 60201.52 59761.01 59244.14 57728.91 56488.74 43228.72 51826.46
## 402 403 404 405 406 407 408 409
## 50825.47 49790.24 56200.56 48732.52 48042.70 46367.02 42038.15 40867.21
## 410 411 412 413 414 415 416 417
## 38928.34 33014.14 40898.08 43828.56 38493.33 33574.62 48467.34 52317.58
## 418 419 420 421 422 423 424 425
## 56252.43 48710.90 45028.99 43703.09 47294.65 35697.31 35426.14 29676.77
## 426 427 428 429 430 431 432 433
## 35274.55 43613.99 50515.93 47276.78 44341.30 41262.11 41150.14 37622.93
## 434 435 436 437 438 439 440 441
## 33755.82 30985.92 32564.26 34416.28 32418.05 37291.15 43505.52 40253.25
## 442 443 444 445 446 447 448 449
## 39958.82 42969.53 40852.59 44847.79 40087.91 31107.39 29943.20 41316.34
## 450 451 452 453 454 455 456 457
## 40997.43 46656.69 42286.59 42636.75 44259.57 47983.09 37842.88 42699.10
## 458 459 460 461 462 463 464 465
## 38115.81 45695.69 49220.77 51845.08 48569.94 50914.08 51115.90 52865.44
## 466 467 468 469 470 471 472 473
## 52362.32 55310.30 52634.75 57693.17 50943.44 48550.94 47135.07 43754.58
## 474 475 476 477 478 479 480 481
## 47516.09 54990.13 49409.09 51114.77 45896.14 44272.82 47109.82 36508.85
## 482 483 484 485 486 487 488 489
## 30061.17 31933.18 34633.72 36125.83 37093.07 30724.74 43272.95 49942.70
## 490 491 492 493 494 495 496 497
## 56782.79 51486.68 56326.66 63971.28 67788.15 54024.76 44589.11 42611.25
## 498 499 500 501 502 503 504 505
## 42934.92 43727.21 38206.03 40608.40 45917.51 51607.27 52317.96 52428.68
## 506 507 508 509 510 511 512 513
## 46121.58 47463.48 43717.34 46476.74 46141.95 39848.73 40981.43 40155.40
## 514 515 516 517 518 519 520 521
## 41262.61 43906.41 36736.29 32008.91 55932.76 64105.12 67763.28 61132.90
## 522 523 524 525 526 527 528 529
## 62474.29 76079.35 83066.02 57980.47 52843.19 49532.81 53918.04 53423.67
## 530 531 532 533 534 535 536 537
## 43593.24 48592.18 61270.74 55783.80 59186.28 63179.73 60228.13 55251.81
## 538 539 540 541 542 543 544 545
## 48704.78 47358.76 55307.17 55057.24 47606.69 49833.48 49659.90 50354.72
## 546 547 548 549 550 551 552 553
## 40990.43 32814.57 37162.56 45290.67 45087.35 46795.65 40796.98 49822.74
## 554 555 556 557 558 559 560 561
## 50979.68 40744.37 50298.86 58172.80 57536.84 61139.64 56900.88 68678.04
## 562 563 564 565 566 567 568 569
## 85390.29 75467.26 64014.64 68440.11 66561.85 67750.16 59308.25 43274.70
## 570 571 572 573 574 575 576 577
## 50293.24 56206.69 57391.13 59469.58 60117.54 57239.92 69503.83 58862.24
## 578 579 580 581 582 583 584 585
## 52576.39 60188.64 61694.75 54779.06 61054.42 56624.33 53664.73 67253.92
## 586 587 588 589 590 591 592 593
## 52661.99 60016.56 59107.68 52821.08 52125.26 52401.69 43104.50 45905.26
## 594 595 596 597 598 599 600 601
## 40524.17 44775.93 53551.51 46873.38 52740.60 55121.72 60798.73 56951.49
## 602 603 604 605 606 607 608 609
## 61771.98 53328.82 55211.45 55988.87 58300.26 58875.11 58415.23 52584.51
## 610 611 612 613 614 615 616 617
## 59656.03 57713.42 54806.55 51508.04 44461.14 55987.50 59880.35 50803.10
## 618 619 620 621 622 623 624 625
## 61219.62 65410.40 58891.11 81149.65 66251.68 58570.32 60576.04 55922.89
## 626 627 628 629 630 631 632 633
## 46244.80 56967.11 37496.34 37356.38 46937.99 57435.24 55477.13 84246.09
## 634 635 636 637 638 639 640 641
## 74418.66 76622.46 78262.03 72981.04 65690.20 62319.84 50208.01 48574.06
## 642 643 644 645 646 647 648 649
## 47464.10 45944.25 44326.68 47007.22 51631.01 66622.58 81066.80 78186.18
## 650 651 652 653 654 655 656 657
## 79091.19 84970.77 98433.99 93185.37 63414.42 60862.72 57812.89 58798.88
## 658 659 660 661 662 663 664 665
## 55234.94 45634.46 48022.08 52345.82 51536.91 63114.63 62992.16 63282.08
## 666 667 668 669 670 671 672 673
## 51721.61 53083.01 54102.86 49435.21 43406.67 46445.75 44041.25 47532.84
## 674 675 676 677 678 679 680 681
## 45282.18 38109.93 32760.58 32758.09 35510.86 40249.37 42512.90 40514.68
## 682 683 684 685 686 687 688 689
## 40538.30 40987.68 35322.29 41660.38 41157.13 41456.80 43455.03 54177.47
## 690 691 692 693 694 695 696 697
## 62646.37 70706.64 59988.82 55991.12 52869.19 58030.33 48309.88 42000.29
## 698 699 700 701 702 703 704 705
## 35887.64 32602.25 31080.15 36594.33 34855.54 35529.11 41470.18 70168.90
## 706 707 708 709 710 711 712 713
## 76335.91 93907.05 90221.27 92820.09 107865.77 106704.82 84035.77 84383.55
## 714 715 716 717 718 719 720 721
## 75710.07 72808.84 70782.62 53488.78 48880.86 52377.69 49879.10 38979.45
## 722 723 724 725 726 727 728 729
## 44554.99 40821.22 43069.38 40941.69 31636.38 35592.47 36218.55 29845.47
## 730 731 732 733 734 735 736 737
## 47937.61 50188.27 48219.53 53881.05 46277.42 46552.34 54544.50 40977.81
## 738 739 740 741 742 743 744 745
## 37385.00 45897.64 42667.86 44172.47 46944.87 46056.85 42471.16 49469.33
## 746 747 748 749 750 751 752 753
## 44483.76 65474.01 70801.36 66905.51 58830.12 78635.43 71700.50 70618.28
## 754 755 756 757 758 759 760 761
## 55835.91 104620.62 121713.76 126308.81 107810.28 110241.77 109488.22 107514.86
## 762 763 764 765 766 767 768 769
## 60130.16 45161.46 47073.21 45695.94 43669.85 152384.47 156827.18 182064.69
## 770 771 772 773 774 775 776 777
## 185635.88 179565.60 177560.37 184451.57 81534.30 72111.14 38441.72 41879.19
## 778 779 780 781 782 783 784 785
## 42288.71 46692.18 41083.65 41404.07 41246.48 45806.04 40536.42 40233.50
## 786 787 788 789 790 791 792 793
## 45354.16 48382.86 46635.07 43980.89 46723.17 50095.16 50501.56 45038.12
## 794 795 796 797 798 799 800 801
## 41068.91 41654.63 42065.27 36690.80 36772.66 36044.35 35934.88 47552.21
## 802 803 804 805 806 807 808 809
## 50284.99 56906.13 59057.44 53614.62 60786.11 68530.08 57386.00 50451.07
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8119
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 4.950703 0.7635253 3.812596
## t2* 2616.107562 165.9669999 872.189714
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.09573 4.898519 13.05503
## 2 lag_depvar 1590.16984 2656.288684 4414.00949Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Jan 27 00:50:38 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Jan 27 00:50:46 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Jan 27 00:50:53 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Jan 27 00:51:01 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Jan 27 00:51:09 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Jan 27 00:51:17 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Jan 27 00:51:25 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Jan 27 00:51:33 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Jan 27 00:51:41 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Jan 27 00:51:49 2025
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | NA | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | NA | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | NA | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | NA | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | NA | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | NA | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | NA | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | NA | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | NA | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | NA | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | NA | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | NA | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | NA | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 0 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2596, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)## Warning in bats(as.numeric(y), use.box.cox = use.box.cox, use.trend =
## use.trend, : optim() did not converge.fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2025-02-09 00:04:58 sería de: 26.545 pesos// Percentil 95% más alto proyectado: 34.743,54
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26175.89 | 26345.07 |
| Lo.80 | 26303.19 | 26502.20 |
| Point.Forecast | 26545.35 | 26799.01 |
| Hi.80 | 31225.49 | 31922.80 |
| Hi.95 | 34028.34 | 34635.16 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(0,1,2)
##
## Coefficients:
## ma1 ma2
## -0.5604 -0.3058
## s.e. 0.1105 0.1108
##
## sigma^2 = 37825: log likelihood = -467.86
## AIC=941.72 AICc=942.09 BIC=948.47paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(0,0,1) errors
##
## Coefficients:
## ma1 intercept xreg
## 0.3568 435.9187 19.2322
## s.e. 0.1020 280.3738 8.6836
##
## sigma^2 = 35837: log likelihood = -471.56
## AIC=951.12 AICc=951.72 BIC=960.17forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 696.4208 | 723.4786 | 766.4502 |
| Lo.80 | 832.7768 | 867.6069 | 870.0364 |
| Point.Forecast | 1090.3596 | 1139.8716 | 1105.4076 |
| Hi.80 | 1347.9423 | 1431.8466 | 1423.9349 |
| Hi.95 | 1484.2984 | 1586.4089 | 1628.1543 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [4] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.9
## [7] tidytext_0.4.1 DT_0.32 janitor_2.2.0
## [10] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [13] xts_0.13.2 forecast_8.21.1 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-11
## [19] NLP_0.2-1 tsibble_1.1.4 lubridate_1.9.3
## [22] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.2
## [25] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [28] gsynth_1.2.1 lattice_0.20-45 GGally_2.2.1
## [31] ggplot2_3.5.0 gridExtra_2.3 plotrix_3.8-4
## [34] sparklyr_1.8.4 httr_1.4.7 readxl_1.4.3
## [37] zoo_1.8-12 stringr_1.5.1 stringi_1.8.3
## [40] data.table_1.15.0 reshape2_1.4.4 fUnitRoots_4021.80
## [43] plyr_1.8.9 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] uuid_1.2-0 systemfonts_1.0.5 selectr_0.4-2
## [4] lazyeval_0.2.2 websocket_1.4.1 crosstalk_1.2.1
## [7] listenv_0.9.1 digest_0.6.34 foreach_1.5.2
## [10] htmltools_0.5.7 fansi_1.0.6 ggfortify_0.4.16
## [13] magrittr_2.0.3 doParallel_1.0.17 tzdb_0.4.0
## [16] globals_0.16.2 vroom_1.6.5 sandwich_3.1-0
## [19] askpass_1.2.0 timechange_0.3.0 anytime_0.3.9
## [22] tseries_0.10-55 colorspace_2.1-0 xfun_0.42
## [25] crayon_1.5.2 jsonlite_1.8.8 iterators_1.0.14
## [28] glue_1.7.0 gtable_0.3.4 car_3.1-2
## [31] quantmod_0.4.26 abind_1.4-5 mvtnorm_1.2-4
## [34] DBI_1.2.2 rngtools_1.5.2 Rcpp_1.0.12
## [37] lfe_2.9-0 viridisLite_0.4.2 xtable_1.8-4
## [40] bit_4.0.5 Formula_1.2-5 htmlwidgets_1.6.4
## [43] timeSeries_4032.109 gplots_3.1.3.1 ellipsis_0.3.2
## [46] spatial_7.3-14 farver_2.1.1 pkgconfig_2.0.3
## [49] nnet_7.3-16 sass_0.4.8 dbplyr_2.4.0
## [52] chromote_0.2.0 utf8_1.2.4 labeling_0.4.3
## [55] tidyselect_1.2.0 rlang_1.1.3 later_1.3.2
## [58] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [61] cachem_1.0.8 cli_3.6.2 generics_0.1.3
## [64] evaluate_0.23 fastmap_1.1.1 yaml_2.3.8
## [67] processx_3.8.3 knitr_1.45 bit64_4.0.5
## [70] caTools_1.18.2 future_1.33.1 nlme_3.1-153
## [73] doRNG_1.8.6 slam_0.1-50 xml2_1.3.6
## [76] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.15.0
## [79] curl_5.2.0 bslib_0.6.1 highr_0.10
## [82] ps_1.7.6 fBasics_4032.96 Matrix_1.6-5
## [85] its.analysis_1.6.0 urca_1.3-3 vctrs_0.6.5
## [88] pillar_1.9.0 lifecycle_1.0.4 lmtest_0.9-40
## [91] jquerylib_0.1.4 bitops_1.0-7 R6_2.5.1
## [94] promises_1.2.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [97] parallelly_1.37.0 codetools_0.2-18 ggstats_0.5.1
## [100] assertthat_0.2.1 boot_1.3-28 gtools_3.9.5
## [103] MASS_7.3-54 openssl_2.1.1 withr_3.0.0
## [106] fracdiff_1.5-3 parallel_4.1.2 hms_1.1.3
## [109] quadprog_1.5-8 timeDate_4032.109 rmarkdown_2.25
## [112] snakecase_0.11.1 carData_3.0-5 TTR_0.24.4#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))